RealtimeData
Workflow and IT automation

Workflow automation and IT automation with AI agents

Business process automation that goes past RPA: agents that find the data, check what the terms mean, act, and record every step. Built to pass review and to survive the person who built it leaving.

workflow automationit automationbusiness process automationrpaintelligent automationit process automation
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Why most of it never goes live

Classic RPA breaks the day a screen changes, and a script nobody documented becomes a risk the day its author leaves. Intelligent automation fails differently: an agent that improvises on an unusual case does damage confidently. Both problems have the same fix, an agent that is grounded in a recorded picture of the system it drives and that stops when the picture and the screen disagree.

The fix is the same five phase method behind every one of the twenty-seven systems on the work page: someone owns the outcome, the data is found, the definitions are agreed, the real process leaves a record, and only then does anything ship. The free AI readiness diagnostic tells you which phase you are in.

What we build for this

Operations agents behind gates

The agentic operations platform reconciles deposits and posts them to a ledger every single day. Every node that can move money sits behind a gate that fails closed, and the robot driving the vendor console is grounded in a stored atlas of that interface rather than guessing at the screen.

Requirements before automation

Every automation starts as a requirements graph whose leaves each name one function and one test. Nothing ships without an assertion that proves it. See requirements and specification.

Determinism where it matters

Steps that must produce the same answer twice are computed, not generated. Models describe, decide and draft; arithmetic, posting and filing are code with tests.

Runs on your side of the wall

A local model gateway serves models on hardware we own, so the automation does not depend on a third party API or send your operational data through one.

How it stays trustworthy

Grounded, trustworthy AI is not a slogan here. It is five rules every agent we ship has to pass.

Cites its sourcesEvery answer an agent gives points at the document, record or rule it came from. If it cannot cite it, it does not say it.
Refuses to inventWhen the data is missing or the question is outside what it knows, the agent says so and stops, instead of producing a confident guess.
Stops and asks a personAnything uncertain, unusual or expensive goes to a named person with the evidence attached. The agent never acts on a hunch.
Leaves an evidence trailEvery step records the source, the definition used, the confidence, who approved it and the outcome, so a reviewer or an auditor can see why it acted.
Runs inside your perimeterModels and data run on hardware we own or on yours. Client data does not have to leave the building to be worked on.

The services behind it

From the seven AI consultancy services offered from Barcelona and Alanya, these are the ones this work draws on.

Questions people ask

Is this RPA?

It includes what RPA does, driving screens and moving records, but the driver is grounded in a recorded atlas of the interface and stops when the screen disagrees with it. And the decisions around the driving are made by agents that cite their inputs.

Which systems can it work with?

Anything with an API, a database, a mailbox or a screen. The deposit platform drives a vendor console that has no API at all.

How do you keep it from breaking silently?

Gates that fail closed, tests that assert the outcome of every step, and an evidence trail that a person reviews. Silence is treated as a failure, not a success.

Who maintains it after you leave?

It is built to survive that. Specification, tests and the evidence trail are the handover, and a fractional retainer is available if you want the architect on call.